Proteinase-Activated Receptor-2 Induction by Neuroinflammation Prevents Neuronal Death during HIV Infection
Bibliographic record
Abstract
Proteinase-activated receptors (PARs), a newly discovered subgroup of G-protein coupled receptors, are widely expressed by neural cells, but their roles in the nervous system remain uncertain. In this study, we report that PAR-2 was up-regulated on neurons in conjunction with neuroinflammation in brain tissue from patients with HIV-1-associated dementia. The inflammatory cytokines TNF-alpha and IL-1beta were also increased in HIV-1-associated dementia brains compared with patients without dementia (p < 0.05), but these same cytokines induced PAR-2 expression on neurons. Enhanced PAR-2 expression and subsequent activation prevented neuronal cell death and induction of the tumor suppressor, p53, caused by the HIV-encoded protein, Tat (p < 0.01). Intrastriatal implantation of a PAR-2 peptide agonist also inhibited Tat-induced neurotoxicity in a mouse model of HIV neuropathogenesis (p < 0.05). Moreover, PAR-2 null animals showed more severe neuroinflammation and neuronal loss caused by Tat neurotoxicity (p < 0.05). TNF-alpha protected wild-type neurons from Tat-related neurotoxicity, but in PAR-2-deficient neurons, the same concentrations of TNF-alpha were cytotoxic (p < 0.001). Thus, neuroinflammation can exert protective effects by which it induces PAR-2 expression with the ensuing abrogation of neuronal death.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".